Dynamic Graph Embedding via Meta-Learning

Graphs in real-world applications usually evolve constantly presenting dynamic behaviors such as social networks and transportation networks. Hence, dynamic graph embedding has gained much attention recently. In dynamic graphs, both the topology and node attributes could change over time, which pose great challenges for developing effective embedding models. Typically, the evolution process of a dynamic graph can be recorded as a series of snapshots. We observe that the evolution process inherently provides both prior information (previous snapshots) and validation information (the next snapshot). The prior information can be used to fit the evolution process, while the validation information can be used to improve the generalization ability of a graph embedding model. However, existing dynamic graph embedding models only utilize the prior information, but overlook the validation information. To tackle this issue, this paper proposes a novel dynamic graph embedding method via Model-Agnostic Meta-Learning, which utilizes both kinds of information to obtain better graph representation. The extensive experiments on eight real-world datasets demonstrate the superiority of our proposed method over state-of-the-art methods on various graph analysis tasks.